Applied Scientist, Generative AI, AGI Inference Engine

The Artificial General Intelligence (AGI) Inference Engine team is looking for a highly-skilled Senior Applied Scientist, to lead the research and implementation of novel techniques to push the boundaries of efficient inference for Generative Artificial Intelligence (GenAI) models.

Key job responsibilities

Explore emerging inference optimization techniques for LLMs and diffusion models

Design and conduct experiments, analyze results and influence Amazon AGI roadmap by providing recommendations

Collaborate with cross-functional teams of engineers and scientists to identify and solve complex problems in GenAI

Think big about the arc of development of Gen AI over a multi-year horizon, and identify new opportunities to apply these technologies to solve real-world problems

Communicate results and insights to both technical and non-technical audiences, including through presentations and written reports

A day in the life

Applied Scientists in our team are independent and have much freedom in the way they contribute. They read recent papers, explore ideas, brainstorm and freely connect with others to cooperate. They detect, investigate and resolve complex problems, sometimes through creating software solutions, and sometimes just through finding and reusing something that exists. They partner with leaders to focus on right things and make good decisions.

About the team

Our mission is to build best-in-class, fast, accurate, and cost-efficient large language and diffusion model inference solutions and infrastructure that will enable Amazon businesses to deliver more value to their customers.

BASIC QUALIFICATIONS

2+ years of research experience

PhD, or Master's degree and 3+ years of applied research experience

Experience with neural deep learning methods and machine learning

Knowledge of Machine Learning algorithms and techniques

PREFERRED QUALIFICATIONS

PhD in Computer Science, Machine Learning, or a related field, with a focus on GenAI

Experience in developing and implementing algorithms for LLMs and / or diffusion models inference

Programming skills in Python and experience with deep learning frameworks such as Tensor Flow or PyTorch

Excellent problem-solving skills, with the ability to think creatively and critically about complex problems

Strong communication and collaboration skills, with the ability to work effectively with cross-functional teams

Experience with patents or publications at top-tier peer-reviewed conferences or journals